A Sequence-to-Sequence Transformer Model for Satellite Retrieval of Aerosol Optical and Microphysical Parameters from Space DOI Creative Commons
Luo Zhang, Haoran Gu, Zhengqiang Li

et al.

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(24), P. 4659 - 4659

Published: Dec. 12, 2024

Aerosol optical and microphysical properties determine their radiative capabilities, climatic impacts, health effects. Satellite remote sensing is a crucial tool for obtaining aerosol parameters on global scale. However, traditional physical statistical retrieval methods face bottlenecks in data mining capacity as the volume of satellite observation information increases rapidly. Artificial intelligence are increasingly applied to parameter retrieval, yet most current approaches focus end-to-end single-parameter without considering inherent relationships among multiple properties. In this study, we propose sequence-to-sequence joint algorithm based transformer model S2STM. Unlike conventional methods, leverages encoding–decoding capabilities model, coupling multi-source such polarized satellite, meteorological, surface characteristics, incorporates physically coherent consistency loss function. This approach transforms numerical regression into relationship mapping. We observations from Chinese polarimetric (the Particulate Observing Scanning Polarimeter, POSP) simultaneously retrieved key parameters. Event analyses, including dust pollution episodes, demonstrate method’s responsiveness hotspot regions events. The results show good agreement with ground-based products. method also adaptable instruments various configurations (e.g., multi-wavelength, multi-angle, multi-dimensional polarization) can further improve its spatiotemporal generalization performance by enhancing spatial balance ground station training datasets.

Language: Английский

A novel physics-based cloud retrieval algorithm based on neural networks (CRANN) from hyperspectral measurements in the O2-O2 band DOI
Wenwu Wang, Husi Letu,

Huazhe Shang

et al.

Remote Sensing of Environment, Journal Year: 2024, Volume and Issue: 311, P. 114267 - 114267

Published: June 25, 2024

Language: Английский

Citations

0
Random Forest Model-based Aerosol Optical Depth Inversion and Variation Analysis in China DOI

Lejun ZHAO,

Fuxing Li,

L Wang

et al.

Deleted Journal, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 1, 2024

Language: Английский

Citations

0
The Aerosol Optical Depth Retrieval from Wide-Swath Imaging of DaQi-1 over Beijing DOI Creative Commons
Zhongting Wang, Ruijie Zhang, Ruizhi Chen

et al.

Atmosphere, Journal Year: 2024, Volume and Issue: 15(12), P. 1476 - 1476

Published: Dec. 10, 2024

The Wide-Swath Imaging (WSI) sensor is a Chinese satellite launched in 2022, capable of providing data at resolutions ranging from 75 to 600 m for monitoring aerosols, fire points, and dust, among other uses. In this study, we developed Dark Dense Vegetation method retrieve the Aerosol Optical Depth (AOD) quickly WSI data. First, after splitting into three types according Normalized Difference Index (NDVI), calculated empirical parameters land reflectance between red (0.65 μm) blue (0.47 channels using Moderate Resolution Spectroradiometer (MODIS) products over Beijing area. Second, decrease NDVI was simulated analyzed under different AODs solar zenith angles, introduced an iterative inversion approach account it. simulation retrievals demonstrated that produced accurate results less than four iterations. Thirdly, utilized atmospherically corrected dark target identification output AOD result. Finally, retrieval experiments were conducted collected 2023. retrieved images highlighted two air pollution events occurring during 3–8 March 27–31 October 2023 showed strong correlation with Robotic Network station (the coefficient greater 0.9). Our exhibited accuracy MODIS aerosol product, though it Multi-Angle Implementation Atmospheric Correction product.

Language: Английский

Citations

0
A Sequence-to-Sequence Transformer Model for Satellite Retrieval of Aerosol Optical and Microphysical Parameters from Space DOI Creative Commons
Luo Zhang, Haoran Gu, Zhengqiang Li

et al.

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(24), P. 4659 - 4659

Published: Dec. 12, 2024

Aerosol optical and microphysical properties determine their radiative capabilities, climatic impacts, health effects. Satellite remote sensing is a crucial tool for obtaining aerosol parameters on global scale. However, traditional physical statistical retrieval methods face bottlenecks in data mining capacity as the volume of satellite observation information increases rapidly. Artificial intelligence are increasingly applied to parameter retrieval, yet most current approaches focus end-to-end single-parameter without considering inherent relationships among multiple properties. In this study, we propose sequence-to-sequence joint algorithm based transformer model S2STM. Unlike conventional methods, leverages encoding–decoding capabilities model, coupling multi-source such polarized satellite, meteorological, surface characteristics, incorporates physically coherent consistency loss function. This approach transforms numerical regression into relationship mapping. We observations from Chinese polarimetric (the Particulate Observing Scanning Polarimeter, POSP) simultaneously retrieved key parameters. Event analyses, including dust pollution episodes, demonstrate method’s responsiveness hotspot regions events. The results show good agreement with ground-based products. method also adaptable instruments various configurations (e.g., multi-wavelength, multi-angle, multi-dimensional polarization) can further improve its spatiotemporal generalization performance by enhancing spatial balance ground station training datasets.

Language: Английский

Citations

0